DALY (version 1.2.0)

sensitivity: Perform DALY sensitivity analysis

Description

This function performs a probabilistic global sensitivity analysis of the overall DALY estimate. Three methods are available, i.e., based on mapped regression coefficients, standardized regression coefficients, and Spearman's rank correlation coefficients. See below for more details.

Usage

sensitivity(x, method = c("regression", "rank"), mapped = TRUE,
            plot = TRUE, main = "Sensitivity analysis", ...)

Arguments

x

Object of class 'DALY', typically obtained from a call to getDALY

method

Sensitivity analysis method: "regression" or "rank". See below for details

mapped

Should mapped regression coefficients be presented? Defaults to TRUE

plot

Should a barplot of ranked significant estimates be shown? Defaults to TRUE

main

Main title of the barplot

...

Additional arguments to be passed to barplot

Value

• If method = "regression", the output of summary.lm; If method = "rank", a matrix with columns for Spearman's rank correlation coefficient (rho) and corresponding p-value.

Details

Sensitivity analysis studies how the uncertainty in the overall DALY estimate can be apportioned to the different sources of uncertainty in the input parameters. These results can therefore help to identify those input parameters that cause significant uncertainty in the overall DALY estimate and that therefore may be the focus of further research if one wishes to reduce the uncertainty in the overall estimate. The sensitivity function implements a probabilistic global sensitivity analysis, in which the analysis is conducted over the full range of plausible input values (hence global), determined by the specified uncertainty distributions (hence probabilistic). Specifying method = "regression" will perform a linear regression-based sensitivity analysis. Here, the simulated overall DALY estimates will be regressed against the simulated values for the stochastic input parameters (using lm). To facilitate comparison, the independent terms are standardized such that they are normally distributed with mean zero and standard deviation one (using scale). If mapped = TRUE, the dependent term is not standardized, such that the resulting mapped regression coefficients correspond to the change in overall DALY given one standard deviation change in the corresponding input parameter. If mapped = FALSE, the dependent term is standardized, such that the resulting standardized regression coefficients correspond to the number of standard deviations change in overall DALY given one standard deviation change in the corresponding input parameter. Specifying method = "rank" will perform a rank correlation-based sensitivity analysis. Here, Spearman's rank correlation coefficients will be calculated between the simulated overall DALY estimates and the simulated values for each of the stochastic input parameters (using cor.test).

References

• Vose D (2000).Risk analysis: a quantitative guide. John Wiley & Sons.

See Also

getDALY lm cor.test

Examples

##= load NCC example ======================================
setDALYexample(1)

##= perform DALY calculation, store results in 'x' ========
x <- getDALY()

##= perform DALY sensitivity analysis =====================

# mapped regression coefficients
sensitivity(x, method = "regression", mapped = TRUE)

# standardized regression coefficients
sensitivity(x, method = "regression", mapped = FALSE)

# Spearman's rank correlation coefficients
sensitivity(x, method = "rank")